To solve the problem of LiDAR's low accuracy in detecting similar objects and distant small targets, we designs a complementary 3D object detection network for cameras and lidar, the Multi-scale Dynamic Feature Voxel to Point (MDVP-RCNN). MDVP-RCNN is a two-stage 3D object detection network that uses point clouds as nodes, integrating point cloud features and image information onto the point cloud. In the first stage of MDVP-RCNN, the raw point cloud is downsampled to a fixed number of key points via Farthest Point Sampling (FPS), then sparse convolutions and deformable convolutions are used as the backbone network for voxel feature extraction. A dual-channel attention mechanism is introduced in the bird's-eye view (BEV), sequentially learning the essential characteristics of the pseudo-2D image and compensating for the lost features during the 2Dization of the point cloud. In the second stage, a feature aggregation module combines the color information of the image with the point cloud information in a weighted manner. Experimental results show that the network performs excellently on small targets, with Average Precision (AP) of 61.76%, 67.66%, and 82.36% respectively achieved for pedestrian, cyclist, and car.Code is available at https://github.com/3623687277/MDVP-RCNN

Image disturbances caused by platform ego-motion are coupled with true target motion under the dynamic viewpoints of unmanned ground vehicles (UGVs), resulting in trajectory-prediction drift, association mismatches, and persistent contamination of identity information by unreliable observations. This paper proposes ERM-Track, a disturbance-aware and reliability-guided online multi-object tracking framework. I²DF-Mamba uses a causal dual-stream Mamba encoder to integrate IMU, joint-state, and trajectory histories and predicts a trajectory-specific image-disturbance distribution. The predicted disturbance mean and uncertainty are mapped explicitly from normalized/log-scale disturbance coordinates to the detection-observation space. CF-TUR then estimates observation reliability through reliable–contaminated posterior fusion and causal evidence accumulation and generates separate bounded write gains for the motion state and identity memory. On the 6488-frame sealed holdout set of the additionally annotated CEAR data, ERM-Track obtains 64.34% HOTA, 66.28% AssA, and 73.42% IDF1, with 28 identity switches. Three-seed backbone replacement experiments show that Mamba provides the highest mean HOTA among the evaluated causal encoders. Post hoc isotonic calibration reduces ECE from 0.4752 to 0.0478, with only marginal changes in the tracking metrics. The complete pipeline reaches 69.50 FPS on an RTX 4080 workstation and an onboard mean latency of approximately 29 ms on a Jetson Orin NX, corresponding to a reciprocal processing rate of approximately 34.5 FPS.